See which leads are most likely to convert
Your team can't give every lead the same attention. Predictive models use your own data to show who is most likely to convert and who may be worth the most.
What is predictive lead scoring?
Predictive lead scoring uses a statistical model, trained on your past leads and their outcomes, to estimate how likely each new lead is to convert. Propensity modeling applies the same idea to other actions, such as buying again or staying a customer.
Why rules-based scoring falls short
Traditional lead scoring adds points for actions someone on your team thinks matter, such as opening an email or visiting a pricing page. Those rules are based on opinion and can go stale.
A predictive model learns from what actually happened with your past leads. It can weigh many signals at once and be retrained as your market changes, though it's only as good as the data it learns from.
What's included
Predictive lead scoring
Score each new lead based on how similar leads have performed, so your team and your automation can prioritize.
Conversion probability modeling
Estimate the likelihood that a lead or visitor will take a specific next step, such as becoming qualified or closing.
Customer lifetime value prediction
Estimate what a customer may be worth over time, so you can focus on the people who bring long-term value.
Audience propensity modeling
Find the groups most likely to act, and build audiences you can use in campaigns and journeys.
How we approach scoring and propensity
1. Define the outcome
Decide exactly what the model should predict and how you'll use the result.
2. Prepare the data
Bring together past leads and their outcomes, and check that the data is reliable.
3. Build and validate
Train the model on past leads, then test it on leads it hasn't seen.
4. Put it to work
Send scores into your CRM, journeys, and ad platforms, where they can change what happens.
How this connects to the loop
From Measurement
Clean lead and CRM outcome data gives the model real examples to learn from.
To Optimization
Scores become signals for bidding, audiences, and lead routing.
Common questions
How much data do we need for predictive lead scoring?
You need enough past leads with known outcomes, including leads that converted and leads that didn't. The right amount depends on your conversion rate and the model, so we check your data first.
How is predictive scoring different from traditional lead scoring?
Traditional scoring uses rules and points chosen by people. Predictive scoring uses a model that learns which signals are linked to real outcomes in your data.
What is customer lifetime value?
Customer lifetime value is an estimate of the total revenue or profit a customer will bring in over the whole relationship with your business.
Can a lead score be wrong?
Yes. Scores are probabilities, not certainties. They help your team prioritize, and they should be monitored and improved over time.
More in Prediction
- Value ModelingPut a value on leads and funnel stages so decisions reflect quality.
- ForecastingProject revenue and demand, and compare budget scenarios before you commit.
- Attribution & IncrementalityFind out what marketing really caused, using lift tests and mix modeling.
- Model OperationsMonitor your models and retrain them so they stay accurate.
Talk to us about scoring and propensity
Tell us where your marketing stands today, and we'll show you what to do first.